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Kian Hong Kock

Researcher at Brigham and Women's Hospital

Publications -  5
Citations -  360

Kian Hong Kock is an academic researcher from Brigham and Women's Hospital. The author has contributed to research in topics: Gene & Gene expression. The author has an hindex of 3, co-authored 3 publications receiving 256 citations. Previous affiliations of Kian Hong Kock include Harvard University.

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Journal ArticleDOI

Transcription factor–DNA binding: beyond binding site motifs

TL;DR: Novel approaches for characterizing functional binding site motifs that promise to inform the understanding of how TF binding controls gene expression and ultimately contributes to phenotype are highlighted.
Journal ArticleDOI

Survey of variation in human transcription factors reveals prevalent DNA binding changes

TL;DR: In this article, a computational, structure-based approach was developed to evaluate TF variants for their impact on DNA binding activity and used universal protein-binding microarrays to assay sequence-specific DNA binding activities across 41 reference and 117 variant alleles found in individuals of diverse ancestries and families with Mendelian diseases.
Journal ArticleDOI

Common variants in signaling transcription-factor-binding sites drive phenotypic variability in red blood cell traits

TL;DR: It is proposed that the majority of the RBC-trait-associated variants that reside on transcription-factor-binding sequences fall in STF target sequences, suggesting that the phenotypic variation of RBC traits could stem from altered responsiveness to extracellular stimuli.
Posted ContentDOI

DNA binding analysis of rare variants in homeodomains reveals novel homeodomain specificity-determining residues

TL;DR: In this article , the second largest class of DNA binding domains (DBDs) among eukaryotic sequence-specific transcription factors (TFs) were analyzed for their effects on DNA binding activity.
Posted ContentDOI

Monopogen: single nucleotide variant calling from single cell sequencing

TL;DR: Monopogen as mentioned in this paper leverages linkage disequilibrium from external reference panels to identify germline SNVs from sparse sequencing data and uses Monovar to identify novel SNVs at cluster (or cell type) levels.